Load libraries
This project uses renv
to keep track of installed packages. Install renv if not
installed and load dependencies with renv::restore().
install.packages("renv")
renv::restore()
library(readr)
library(dplyr)
library(tidyr)
library(reshape2)
library(GenomicRanges)
library(pheatmap)
library(tibble)
library(ggplot2)
library(stringr)
library(cowplot)
library(markdown)
library(RColorBrewer)
library(GenomicAlignments)
library(reshape2)
Read data
- Get list of samples
samples <- read_tsv("config/samples.tsv", show_col_types = FALSE)
units <- read_tsv("config/units.tsv", show_col_types = FALSE)
sample_units <- dplyr::left_join(samples, units, by = "sample_name") %>%
unite(sample_unit, sample_name, unit_name, remove = FALSE)
sample_units
- Read Samtools
idxstats to get human coverage for
normalization
Notes:
- The counts include the total number of reads aligned, they are not
limited to uniquely aligned reads.
- The counts are reads, not pairs or fragments
idxstats_exogenousrna_dir <-
"results/samtools_idxstats/exogenous_rna/"
idxstats_human_dir <-
"results/samtools_idxstats/Homo_sapiens.GRCh38.dna.primary_assembly/"
bowtie2_human_logs <-
"results/logs/bowtie2/Homo_sapiens.GRCh38.dna.primary_assembly/"
idxstats <- tibble()
for (row in seq_len(nrow(sample_units))) {
sample <- sample_units[row, ]$sample_unit
# Read `idsxstats` for exogenous mapped reads
exogenous_rna_stats <- read_tsv(
file.path(idxstats_exogenousrna_dir, sprintf("%s.bam.idxstats", sample)),
col_names = c(
"sequence_name", "sequence_length",
"mapped_reads", "unmapped_reads"
),
col_types = "ciii"
)
exogenous_rna_mapped_reads <- exogenous_rna_stats %>%
filter(!sequence_name %in% c("*")) %>%
select(sequence_name, mapped_reads) %>%
mutate(sample = sample)
# Read `idxstats` for human mapped reads
human_stats <- read_tsv(
file.path(idxstats_human_dir, sprintf("%s.bam.idxstats", sample)),
col_names = c(
"sequence_name", "sequence_length",
"mapped_reads", "unmapped_reads"
),
col_types = "ciii"
)
grch38_mapped_reads <- human_stats %>%
filter(!sequence_name %in% c("*")) %>%
select(mapped_reads) %>%
sum()
grch38_mapped_reads <- tibble(
sequence_name = "grch38_mapped_reads",
mapped_reads = grch38_mapped_reads,
sample = sample
)
# Read bowtie2 logs for unmapped reads
bowtie2_log <- readLines(
file.path(bowtie2_human_logs, sprintf("%s.log", sample))
)
total_pairs <- strtoi(str_split(bowtie2_log[1], " ")[[1]][1])
total_reads <- total_pairs * 2
unmapped_reads <- tibble(
sequence_name = "unmapped",
mapped_reads = total_reads - grch38_mapped_reads$mapped_reads,
sample = sample
)
# Consolidate counts for rows
idxstats <- rbind(
idxstats,
exogenous_rna_mapped_reads,
grch38_mapped_reads,
unmapped_reads
)
}
idxstats
- Read
bedpe files to get exogenous rna coverage of
paired reads
bedpe_data <- tibble()
for (sample in sample_units$sample_unit) {
data <-
readr::read_tsv(
sprintf(
"results/alignments/exogenous_rna/bedpe/%s.bedpe", sample
),
col_names = c(
"chrom1", "chrom1Start", "chrom1End",
"chrom2", "chrom2Start", "chrom2End",
"name", "score", "strand1", "strand2"
),
col_types = "ciiciicicc"
)
bedpe_data <- tibble(rbind(
bedpe_data,
cbind(
sample = sample,
data
)
))
}
bedpe_data
Coverage
Concordant vs Discordant paired reads
Concordant pairs are pairs of reads that:
- Align on the same pegRNA
- Align within 500 bp of each other
- Align in the expected forward-reverse orientation
(
--> .. <--)
Discordant reads aligned but whose mate:
- Did not align (on the pegRNA)
- Aligned more than 500 bp away
- Aligned in an unexpected orientation
## Config and function definition
bam_dir <- "results/alignments/exogenous_rna/sorted"
last_day <- 0
cols <- brewer.pal(n = 5, name = "RdBu")
concordant_cell_line_colors <- list(
"Parental" = "#CA0020",
"P1E10" = "#0571B0"
)
discordant_cell_line_colors <- list(
"Parental" = "#F4A582",
"P1E10" = "#92C5DE"
)
# Exogenous RNA mixtures
rna_mixes <- tibble()
for (mix in c("mastermix1", "mastermix2")) {
t <- readDNAStringSet(sprintf("data/references/%s.fa", mix))
rna_mixes <- rbind(rna_mixes, tibble(
exogenous_rna = mix,
rna_species = word(t@ranges@NAMES, 1),
length = t@ranges@width
))
}
source("pegrna_plots.R")
VEGFA
rna_mix_rows <- rna_mixes %>% filter(grepl("VEGFA", rna_species))
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (row in seq_len(nrow(rna_mix_rows))) {
rna_species <- rna_mix_rows[[row, "rna_species"]]
mix <- rna_mix_rows[[row, "exogenous_rna"]]
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
mix = mix,
ylab = sprintf("%s coverage (normalized to %s)", mix, norm_factor),
)
}
}




FANCF
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("FANCF", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}


HEK3
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("HEK3", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}




DNMT1
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("DNMT1", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}




RUNX1
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("RUNX1", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}




EMX1
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("EMX1", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}




RNF2
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
for (rna_species in rna_mixes %>%
filter(grepl("RNF2", rna_species)) %>%
pull(rna_species)) {
pegrna_plots(
sequence_name = rna_species,
normalization_factor = norm_factor,
ylab = sprintf("Coverage (normalized to %s)", norm_factor),
)
}
}


---
title: "Adamson smallRNA - pegRNA"
output:
  html_notebook:
    toc: true
    code_folding: hide
---

## Load libraries

This project uses [`renv`](https://rstudio.github.io/renv/articles/renv.html)
to keep track of installed packages. Install `renv` if not installed and load
dependencies with `renv::restore()`.

```r
install.packages("renv")
renv::restore()
```

```{r message=FALSE}
library(readr)
library(dplyr)
library(tidyr)
library(reshape2)
library(GenomicRanges)
library(pheatmap)
library(tibble)
library(ggplot2)
library(stringr)
library(cowplot)
library(markdown)
library(RColorBrewer)
library(GenomicAlignments)
library(reshape2)
```

## Read data

1. Get list of samples

```{r}
samples <- read_tsv("config/samples.tsv", show_col_types = FALSE)
units <- read_tsv("config/units.tsv", show_col_types = FALSE)
sample_units <- dplyr::left_join(samples, units, by = "sample_name") %>%
  unite(sample_unit, sample_name, unit_name, remove = FALSE)
sample_units
```

2. Read Samtools `idxstats` to get human coverage for normalization

Notes:

* The counts include the total number of reads aligned, they 
  are not limited to uniquely aligned reads.
* The counts are reads, not pairs or fragments

```{r}
idxstats_exogenousrna_dir <-
  "results/samtools_idxstats/exogenous_rna/"

idxstats_human_dir <-
  "results/samtools_idxstats/Homo_sapiens.GRCh38.dna.primary_assembly/"

bowtie2_human_logs <-
  "results/logs/bowtie2/Homo_sapiens.GRCh38.dna.primary_assembly/"

idxstats <- tibble()

for (row in seq_len(nrow(sample_units))) {
  sample <- sample_units[row, ]$sample_unit

  # Read `idsxstats` for exogenous mapped reads
  exogenous_rna_stats <- read_tsv(
    file.path(idxstats_exogenousrna_dir, sprintf("%s.bam.idxstats", sample)),
    col_names = c(
      "sequence_name", "sequence_length",
      "mapped_reads", "unmapped_reads"
    ),
    col_types = "ciii"
  )
  exogenous_rna_mapped_reads <- exogenous_rna_stats %>%
    filter(!sequence_name %in% c("*")) %>%
    select(sequence_name, mapped_reads) %>%
    mutate(sample = sample)

  # Read `idxstats` for human mapped reads
  human_stats <- read_tsv(
    file.path(idxstats_human_dir, sprintf("%s.bam.idxstats", sample)),
    col_names = c(
      "sequence_name", "sequence_length",
      "mapped_reads", "unmapped_reads"
    ),
    col_types = "ciii"
  )
  grch38_mapped_reads <- human_stats %>%
    filter(!sequence_name %in% c("*")) %>%
    select(mapped_reads) %>%
    sum()
  grch38_mapped_reads <- tibble(
    sequence_name = "grch38_mapped_reads",
    mapped_reads = grch38_mapped_reads,
    sample = sample
  )

  # Read bowtie2 logs for unmapped reads
  bowtie2_log <- readLines(
    file.path(bowtie2_human_logs, sprintf("%s.log", sample))
  )
  total_pairs <- strtoi(str_split(bowtie2_log[1], " ")[[1]][1])
  total_reads <- total_pairs * 2
  unmapped_reads <- tibble(
    sequence_name = "unmapped",
    mapped_reads = total_reads - grch38_mapped_reads$mapped_reads,
    sample = sample
  )

  # Consolidate counts for rows
  idxstats <- rbind(
    idxstats,
    exogenous_rna_mapped_reads,
    grch38_mapped_reads,
    unmapped_reads
  )
}
idxstats
```

3. Read `bedpe` files to get exogenous rna coverage of paired reads

```{r}
bedpe_data <- tibble()
for (sample in sample_units$sample_unit) {
  data <-
    readr::read_tsv(
      sprintf(
        "results/alignments/exogenous_rna/bedpe/%s.bedpe", sample
      ),
      col_names = c(
        "chrom1", "chrom1Start", "chrom1End",
        "chrom2", "chrom2Start", "chrom2End",
        "name", "score", "strand1", "strand2"
      ),
      col_types = "ciiciicicc"
    )
  bedpe_data <- tibble(rbind(
    bedpe_data,
    cbind(
      sample = sample,
      data
    )
  ))
}
bedpe_data
```

## Coverage

### Concordant vs Discordant paired reads

Concordant pairs are pairs of reads that:

* Align on the same pegRNA
* Align within 500 bp of each other
* Align in the expected forward-reverse orientation (`--> .. <--`)

Discordant reads aligned but whose mate:

* Did not align (on the pegRNA)
* Aligned more than 500 bp away
* Aligned in an unexpected orientation

```{r fig.width=10, fig.height=10}
## Config and function definition

bam_dir <- "results/alignments/exogenous_rna/sorted"

last_day <- 0
cols <- brewer.pal(n = 5, name = "RdBu")

concordant_cell_line_colors <- list(
  "Parental" = "#CA0020",
  "P1E10" = "#0571B0"
)

discordant_cell_line_colors <- list(
  "Parental" = "#F4A582",
  "P1E10" = "#92C5DE"
)

# Exogenous RNA mixtures
rna_mixes <- tibble()
for (mix in c("mastermix1", "mastermix2")) {
  t <- readDNAStringSet(sprintf("data/references/%s.fa", mix))
  rna_mixes <- rbind(rna_mixes, tibble(
    exogenous_rna = mix,
    rna_species = word(t@ranges@NAMES, 1),
    length = t@ranges@width
  ))
}

source("pegrna_plots.R")
```

### VEGFA

```{r fig.width=10, fig.height=10}
rna_mix_rows <- rna_mixes %>% filter(grepl("VEGFA", rna_species))

for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (row in seq_len(nrow(rna_mix_rows))) {
    rna_species <- rna_mix_rows[[row, "rna_species"]]
    mix <- rna_mix_rows[[row, "exogenous_rna"]]

    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      mix = mix,
      ylab = sprintf("%s coverage (normalized to %s)", mix, norm_factor),
    )
  }
}
```

### FANCF

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("FANCF", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```

### HEK3

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("HEK3", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```



#### DNMT1

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("DNMT1", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```


### RUNX1

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("RUNX1", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```

### EMX1

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("EMX1", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```

### RNF2

```{r fig.width=10, fig.height=10}
for (norm_factor in c("exogenous_rna_mapped_reads", "grch38_mapped_reads")) {
  for (rna_species in rna_mixes %>%
    filter(grepl("RNF2", rna_species)) %>%
    pull(rna_species)) {
    pegrna_plots(
      sequence_name = rna_species,
      normalization_factor = norm_factor,
      ylab = sprintf("Coverage (normalized to %s)", norm_factor),
    )
  }
}
```
